The UAV inspection system used for collecting images
includes two subsystems: Skynet for flying drones and capturing images, and the ground center for data processing. The quadcopters are controlled to follow an IoT-communicated formation [1]} to inspect a monorail bridge, as depicted in Fig. REF . Here, all the trainable parameters are initialized by He Normal initialization [2]}. The training is conducted at a learning rate of 1\(e\) -5 and optimized by Adaptive Moment Estimation (Adam) [3]} with the default setting of two hyperparameters, (0.9, 0.999). For performance improvement from using the proposed loss function, the maximum training epoch is set as 30, which is adequately large for the sake of convergence. An early stop is applied when the reduction of the loss between two adjacent epochs is under 0.01%. The training process is conducted on NVIDIA Tesla T4 GPUs 16Gb.
